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Cross-Device Wi-Fi Map Fusion with Gaussian Processes

Hsiao-Chieh Yen,C. Wang

Published 2017 in IEEE Transactions on Mobile Computing

ABSTRACT

spatially sparse received signal strength measurements obtained with multiple devices. First, we show that the residual of the linear regression between devices, usually unaccounted for in existing cross-device localization work, is an important indicator of device dissimilarity and a good predictor of localization performance. Through explicitly modeling the device dissimilarity, one can improve localization accuracy when fusing training sets from multiple devices by weighting each training set differently. Second, we use the Gaussian process (GP) sensor model to develop a regression algorithm which more reliably estimates the linear fit and device dissimilarity given only a few labeled samples from each new device. By accounting for device dissimilarities in map fusion and by using the proposed regression algorithm, localization performance can be greatly improved given just a few training samples from a new device. Also, when fusing multiple existing maps for a new device using regression misfit, performance is improved by 3.5 to 10 percent.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    IEEE Transactions on Mobile Computing

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/TMC.2016.2539966
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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